Age at stroke onset influences the clinical outcome and health‐related quality of life in pediatric ischemic stroke survivors
Bibliographic record
Abstract
AIM: Stroke in children occurs across different phases of brain development. Age at onset may affect outcome and health-related quality of life (HRQL). We evaluated the influence of age at stroke onset on the long-term neurological outcomes and HRQL of pediatric stroke survivors. METHOD: Children with ischemic stroke were recruited into three groups according to their age at onset of stroke (presumed perinatal, neonatal, and childhood). Neurological outcomes were assessed using the Pediatric Stroke Recovery and Recurrence Questionnaire. HRQL was evaluated using proxy report versions (2-18y) of the Pediatric Quality of Life Inventory (PedsQL 4.0). A χ(2) /Fisher's exact test and multivariable logistic regression analysis was performed for the neurological outcomes. HRQL scores from the different age groups were compared using linear regression. RESULTS: Ninety participants (presumed perinatal stroke, n=31; neonatal stroke, n=36; childhood stroke, n=23) were enrolled. Median age at the onset of stroke was 0.5 days and 3.7 years in neonatal and childhood participants respectively. Of the three groups, participants with presumed perinatal stroke demonstrated the worst global (p<0.002) and motor (p<0.001) outcomes and the lowest level of independence in daily activities (p<0.001). Parents reported the best global outcome and overall HRQL (p=0.007) after neonatal stroke. INTERPRETATION: The age at stroke onset has important implications regarding long-term clinical outcomes and HRQL for survivors. Individuals with presumed perinatal stroke should be considered at high-risk for poor outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".